Retrieval-Augmented Generation for Enterprise Knowledge

Course Category : Digital Transformation

An advanced programme for understanding RAG architectures and connecting generative AI models with enterprise knowledge sources to improve response accuracy, reliability, traceability, and governance.
Duration: 5 Days
Level: Advanced

Introduction

The ability to transform distributed organisational knowledge into accessible and contextually relevant information has become a significant capability in enterprise generative AI. Retrieval-Augmented Generation (RAG) provides an architectural approach for connecting Large Language Models with trusted external knowledge sources rather than relying exclusively on knowledge embedded within the model.
This course examines RAG architecture, enterprise knowledge preparation, embeddings and vectors, vector databases, retrieval and ranking mechanisms, prompt construction, output evaluation, security, and governance. It also addresses the architectural considerations required to establish accurate, secure, scalable, and manageable enterprise RAG environments..

Targeted Audience

AI and Generative AI Specialists
Data Engineers and Data Architects
Solution and Enterprise Architects
Knowledge and Enterprise Content Management Professionals
Digital Transformation and Technology Innovation Professionals
IT and Data Managers
Business Analysts Involved in AI Initiatives
Data and AI Governance Professionals

Targeted Skills

Understanding RAG System Architecture
Enterprise Knowledge Preparation and Organisation
Embeddings and Vector Search Concepts
Document Chunking and Indexing Strategy Evaluation
Retrieval, Ranking, and Re-Ranking Design
Retrieved Context Integration with Generation
RAG Quality and Accuracy Evaluation
Security, Access Control, and Governance for AI-Powered Knowledge Systems

Expected Outcomes

Explain the fundamental principles underlying Retrieval-Augmented Generation architecture.
Analyse enterprise knowledge sources and determine preparation requirements for RAG.
Understand the role of embeddings and vector databases in semantic retrieval.
Compare chunking, indexing, retrieval, and re-ranking strategies.
Identify architectural components required to connect retrieved knowledge with Large Language Models.
Evaluate RAG responses according to relevance, accuracy, and grounding criteria.
Identify security, privacy, and access-control risks associated with enterprise knowledge.
Define an integrated architectural and governance approach for enterprise RAG adoption.

Training Topics Index

Retrieval-Augmented Generation concepts within the generative AI ecosystem
LLM knowledge limitations and the challenge of model hallucination
Core RAG pipeline components from knowledge source to generated response
Enterprise knowledge sources documents, policies, manuals, and knowledge bases
RAG use cases for enterprise search, knowledge assistants, and decision support

Document ingestion, content extraction, and metadata organisation
Chunking strategies and their impact on retrieval performance
Embeddings and vector representations of semantic meaning and context
Vector databases, indexing structures, and similarity search mechanisms
Knowledge updates, version management, and index freshness

Semantic, keyword, and hybrid retrieval approaches
Query construction and transformation according to user context
Ranking, re-ranking, and relevance optimisation
Context construction and LLM context-window management
Knowledge-grounded prompt design and source attribution

Retrieval evaluation through precision, relevance, and coverage
Assessing answer grounding and factual consistency
Analysing hallucinations, retrieval failures, and insufficient context
Evaluation datasets and performance metrics for RAG systems
Continuous monitoring and optimisation of retrieval and generation quality

Enterprise architecture and integration with knowledge and data systems
Identity, permissions, and access control for retrieved knowledge
Sensitive information protection, privacy, and data leakage risks
Governance, auditing, traceability, and approved knowledge-source management
Performance, cost, scalability, and long-term RAG sustainability

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

Retrieval-Augmented Generation for Enterprise Knowledge